Recommendation system with user interface for exposing downstream effects of paricular rating actions

ABSTRACT

An interactive system generates personalized item recommendations for users based partly or wholly on ratings assigned by the users to particular items. The system includes an item rating user interface that enables a user to view, prior to rating an item, information regarding the types of items that will be recommended to the user if the user assigns a particular rating or type of rating to the item. The user interface thereby enables users to refrain from performing rating actions that will tend to result in low utility or “poor quality” recommendations from the users&#39; perspectives.

PRIORITY CLAIM

This application is a continuation of U.S. application Ser. No.13/280,593, filed Oct. 25, 2011, the disclosure of which is herebyincorporated by reference.

TECHNICAL FIELD

The present disclosure relates to recommendation and personalizationsystems. More specifically, the disclosure relates to user interfacesfor enabling users to rate items and to obtain item recommendations.

BACKGROUND

Some web sites and other types of interactive systems implementrecommendation services for generating personalized recommendations ofitems stored or represented in a data repository. One common applicationfor recommendation services involves recommending items for purchase,rental, subscription, viewing, or some other form of consumption. Forexample, some e-commerce sites provide services for recommendingproducts to users based wholly or partly on the ratings assigned by theusers to particular items. The recommendations may additionally oralternatively be based on the users' purchase histories, rentalhistories, product viewing histories, item tagging activities, and/orother behavioral profiles. Recommendation services are also commonlyused to recommend web sites, news articles, music and video files,television shows, restaurants, and other types of items.

One problem with existing recommendation services is that a user'sfavorable rating of an item may, in certain circumstances, frequently orprimarily result in recommendations of items the user dislikes. This mayoccur when, for example, the user likes (and thus favorably rates) aparticular item, but generally dislikes the item's category. Forexample, a user may like a particular song or artist in the category ofcountry music, but may generally dislike country music. A favorable itemrating may also undesirably result primarily in recommendations of itemsthe user already owns or is familiar with.

BRIEF DESCRIPTION OF THE DRAWINGS

Specific embodiments and inventive features will now be described withreference to the drawings, which are provided for purposes ofillustration, and not limitation.

FIG. 1 illustrates one embodiment of a user interface that enables auser to rate individual items and to view information regarding thedownstream effects of particular rating actions.

FIG. 2 illustrates the general architecture of an interactive systemthat provides an item rating interface of the type show in FIG. 1.

FIG. 3 illustrates one embodiment of a process that may be implementedby an interactive system, such as the system of FIG. 2, to generate auser interface of the type shown in FIG. 1.

FIG. 4 illustrates an alternative process that may be used in FIG. 3 toidentify the recommended items corresponding to a not-yet-rated item.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

The present disclosure provides a recommendation system and associateduser interface that enable users to rate individual items, such ascatalog items, with knowledge of the downstream effects of particularrating actions. For example, before a user favorably rates a particularitem, the user interface may inform the user of the type or types ofitems that will be recommended to the user as a consequence of thefavorable rating. The user interface thereby enables users to knowingly(1) perform rating actions that will tend to result in high quality itemrecommendations (where “quality” is a measure of how useful therecommendations are to the particular user), and (2) refrain fromperforming rating actions that will tend to result in poor qualityrecommendations.

The user interface may be used with any type of interactive system (suchas a web site, a mobile application based system, or an interactivetelevision system) that generates personalized item recommendations forusers based partly or wholly on users' ratings of particular items. Forexample, the user interface may be part of an interactive system thatprovides functionality for users to purchase, rent, download, and/orstream products selected from an electronic catalog. The products may,for example, include physical products that are shipped to users,digital products (e.g., music tracks, electronic books, videos, mobileapplications, etc.) that are transmitted to users' computing devices, ora combination thereof. The user interface may also be used in systemsthat recommend various other types of items, such as restaurants,television shows, news articles, web sites, magazine subscriptions,authors, blogs, podcasts, musical artists, brands, services, serviceproviders, entertainment events, vacation destinations, and other users.

FIG. 1 illustrates one embodiment of the user interface, and morespecifically, illustrates a personalized item rating web page 26 forrating items represented in an electronic catalog. As will berecognized, the user interface may alternatively be implemented withoutusing web pages. For example, the user interface may be implementedwithin a mobile application (such as a smartphone application), orwithin the native software of an e-book reader or a tablet computingdevice. In this particular example, the item rating page 26 displays aset of book titles and movie titles that have not yet been rated by theuser, and provides a user option to rate each such item. The twoillustrated item categories (books and movies) are merely representativeof the types of items that can be rated.

In the illustrated embodiment, the option to rate each item includes acorresponding “Like” button displayed below the respective item'simage/description. Any of a variety of other types of rating displayelements may additionally or alternatively be provided. For example,rather than displaying “Like” buttons, the user interface may provideinteractive display elements for rating each item on a scale of one tofive stars (or on some other scale). As another example, both “Like” and“Dislike” rating options may be provided for each item.

As depicted in FIG. 1, when the user hovers the mouse cursor or pointer28 over a particular item's graphic 30 or description (or otherwiseselects the item), the user is presented with a popover 32 correspondingto the selected item. (A “popover,” in the context of web pages, is anoverlay display object that is part of the web page, and which can begenerated using executable code included in the web page; see, e.g.,U.S. Pat. No. 7,975,020.) By moving the mouse cursor 28 from item toitem on the page, the user can interactively view the popoverscorresponding to the respective not-yet-rated items 30.

Each popover 32 (one shown) includes a “downstream recommendations” list34 that advantageously notifies the user of one or more types of itemsthat will be recommended to the user if the user favorably rates theselected item 30. As explained below, each such list 34 may bepersonalized for the particular user viewing the page. In theillustrated example, the popover 32 informs the user that “Liking” thebook title “Catching Fire” will cause items like the book titles “TheGirl Who Was on Fire” and “Caribbean Moon” to be recommended. Theparticular items included in the list 34 may not actually be recommendedto the user if the user favorably rates the item 30; however, they areat least representative of the types of items likely to be recommendedas a result of a favorable rating.

Thus, the downstream recommendations list 34 advantageously enables theuser to assess the effect of the rating action before deciding whetherto rate the selected item 30. For example, if the user dislikes thedownstream recommendation items 34, the user may choose not to favorablyrate the selected item 30, even though the user actually likes it. Asanother example, the user may choose not to favorably rate the selecteditem if the user already owns or is very familiar with the recommendeditems. (In general, recommendations tend to be less useful when the useris already familiar with the particular items being recommended.) Thus,the system enables the user to selectively rate items in a manner thatincreases the utility of the recommendations subsequently provided tothe user.

A greater or lesser number of downstream recommendation items 34 (e.g.,one, three, four or five) may alternatively be displayed in each popover32. In addition, although the two downstream recommendation items 34shown in FIG. 4 correspond in item category (namely “books”) to theselected item 30, this need not be the case. For example, the system mayenable other categories of downstream recommendation items (e.g.,movies, music titles, etc.) to be displayed in the popovers for books.

In the embodiment shown in FIG. 1, each popover additionally includes(1) a checkbox for assigning a “not interested” rating to the selecteditem 30, and (2) a checkbox for indicating ownership of the selecteditem 30. The “not interested” and item ownership information collectedby the system through these display elements for a particular user maybe used to refine the personalized item recommendations generated forthe user. For example, a preliminary set of personalized itemrecommendations may be filtered to remove any “already owned” and “notinterested” items, and to remove any items that are similar to theusers' “not interested” items. The “not interested” and “I own it”elements may alternatively be omitted.

The item rating page 26 shown in FIG. 1 represents one example of howthe downstream effects of particular rating actions can be communicatedto users. Numerous variations are possible. The following are examples:

-   -   1. The popovers 32 may include other information about the types        of items that will be recommended if the selected item is        favorably rated. For example, in addition to, or instead of,        displaying a list of would-be recommended items 34, a popover 32        may inform the user more generally of the types or attributes of        items that will be recommended (e.g., “acoustic and classical        music titles” or “mystery novels by authors like . . . ”).    -   2. The user may be given the option to rate particular item        categories or item attributes (e.g., brands, authors, artists,        musical genres, etc.), and may be informed of the downstream        effects of the associated rating actions in the same manner as        described above. For example, a user may be notified that        favorably rating a particular author “will cause books like . .        . to be recommended” or “will cause books from authors like . .        . to be recommended.”    -   3. The user may be presented with multiple rating options for a        selected item (e.g., one to five stars, or “like” and        “dislike”), and may be notified of the downstream effects of        each such option. For example, the “recommended items” list 34        may change automatically depending on which rating option is        currently provisionally selected by the user.    -   4. The “downstream effect” information may be displayed without        the use of popovers. For example, this information may be        displayed at the outset when the page is loaded by the browser,        and thus without the need for the user to mouse-over or        otherwise select particular items 30.    -   5. The popover 32 or other user interface may provide a user        option to favorably rate the item but to request that the rating        not be used for generating recommendations. For example, a        checkbox may be provided with the following messaging: “I like        this item, but do not want it to be used to generate        recommendations for me.” If the user selects this rating option,        the system may increment the item's global “like” count (which        may be displayed on the item's detail page), but may        exclude/disregard this rating when subsequently generating        recommendations for the user.    -   6. Both “Like” and “Dislike” buttons may be displayed for each        non-yet-rated item 30. When the user hovers the cursor 28 over        the “Like” button, a popover 32 similar to the one shown in FIG.        1 may be displayed. When the user hovers the cursor over the        “Dislike” button, a different popover may be displayed which        identifies the types of items that will be filtered out or        otherwise excluded from the user's recommendations as the result        of a Dislike rating.

These and other variations may be included individually, or in anycombination, in embodiments of the invention. In the particularembodiment shown in FIG. 1, when the user clicks on the “Like” buttonfor a particular item, the user's selection is communicated from theuser's computing device to a server, and the item rating page 26 isupdated to reflect the selection. The updates to the page 26 preferablyinclude the following: (1) the rated item is replaced on the page with anew item that has not yet been rated; (2) the running count 36 of thenumber of items “Liked” by the user is incremented, and (3) if thisrating event is the first rating action performed on the page 26, the“Show my new recommendations” link 40, which is initially grayed out,becomes active (selectable) on the page. The page also includes a“refresh and show different items” link 42 that can be selected torefresh the page with a new set of not-yet-rated items.

If/when the user selects the “Show my new recommendations” link 40 onthe item ratings page 26, the user's computing device retrieves anddisplays personalized item recommendations that reflect the item ratingaction(s) performed on the item rating page 26. Typically, these itemrecommendations are based on multiple item rating actions of the user.For example, the recommendations may be based on all rating actionsperformed by the user or a most recent subset of these actions (e.g.,the last N “Liked” items). The personalized recommendations may also bebased on other types of item selection actions recorded by the system,such as user selections of particular items to purchase, rent, view,review, add to a shopping cart, add to a wish list, add to a rentalqueue, or tag. The personalized recommendations are preferably generatedusing a recommendation algorithm that is similar or identical to thatused to generate the downstream recommendations lists 34.

In one embodiment, the personalized item recommendations are presentedin a recommendations popover (not shown) that overlays and hides thenot-yet-rated items on the item rating page 26. The recommendations mayalternatively be presented on a separate page. Each recommendation maybe presented together with an indication of the basis (or bases) for therecommendation. For example, a recommendation of item A may be displayedwith the message “recommended because you said you liked item B,” or“recommended because you said you liked item C and item D.” Afterviewing the item recommendations, the user can close the recommendationspopover and continue rating items.

Item ratings interfaces of the type shown in FIG. 1 may be exposed tousers in various contexts. For example, in the context of a shoppingsite or video rental site, such an interface may be presented when auser first sets up an account with the system. The user interface maythereby enable the system to rapidly collect sufficient items ratingsdata to provide useful item recommendations to new customers. The sitemay also enable users to return to the item ratings interface 26 at anytime to rate additional items.

Popovers 32 (or other user interface display units) of the type shown inFIG. 1 may also be presented on other types of pages that providefunctionality for rating items. For example, each item detail page of anelectronic catalog may include one or more display elements for ratingthe item, and may include an associated popover 32 for enabling the userto selectively view the downstream recommendation effects of positively(or otherwise) rating the item. Popovers 32 of the type shown in FIG. 1may also be exposed on item recommendation pages, item category or“browse node” pages, and other types of pages that include item ratingfunctionality.

FIG. 2 illustrates one embodiment of an interactive system 50 thatprovides the above-described features. The system 50 includes a server52 that provides network-based user access to an electronic catalog ofitems that are available for purchase, rental, download, and/or othertransaction types. The server 52 may include multiple distinct servermachines, such as web server machines. For purposes of illustration, itwill be assumed that the system 50 hosts a web site that providesfunctionality for enabling users to purchase items represented in thecatalog, although this need not be the case. The interactive system 50may be accessed by users via one or more types of user computing devices51, such as personal computers, smartphones, and tablet computers.

As is conventional, the electronic catalog may include a distinct itemdetail page (also referred to as a product detail page) for each item,and may include a hierarchical browse tree for browsing the items byitem category. Information about the various items (product images,descriptions, prices, etc.) is stored in an item database 54 or otherdata repository. The server 52 retrieves item data from the database 54via a catalog service 56, and populates web pages with such item data.The various components for dynamically generating web pages, includingitem rating pages of the type shown in FIG. 1, are represented by theuser interface (UI) block 68 in FIG. 2.

As users browse the electronic catalog and perform various types ofactions (such as rating items, purchasing items, etc.), the systemrecords one or more types of item-related events in a behavioral-eventdata repository 58. This data repository 58 may, in someimplementations, include multiple distinct log files and/or databases.The recorded events may include, for example, item purchase events, itemviewing events (which may be based on visits to item detail pages),“shopping cart add” events, “wish list add” events, item rating events,and/or any other type of user action that evidence users' interests inparticular catalog items or item categories. Some or all types of eventsmay be recorded in association with the corresponding user, such thatitem preference profiles are maintained for specific users.

The recorded events or event histories are analyzed periodically by anassociation mining service 60 to detect behavioral relationships (alsocalled “behavioral associations”) between particular items. For example,if a relatively large fraction of those who purchase item A alsopurchase item B, the association mining service 60 may generate anitem-to-item association mapping between these two items. Theitem-to-item associations may be detected using any of a variety ofmethods that are known in the art, including the methods described inU.S. Pat. Nos. 7,685,074 and 7,827,186, the disclosures of which arehereby incorporated by reference. Although the associations aretypically based on monitored user behaviors, they may additionally oralternatively be based on detected similarities between item attributesor content.

The item-to-item associations detected by the association mining service60 are recorded in a data repository 62 as item-to-item mappings. Theitem-to-item mappings are used by one or more personalization services66, including a recommendation service 68, to provide personalizedcontent to users. Each item-to-item mapping maps a particular “source”item to a related item (or list of related items), and may be used as abasis for recommending the related item(s) to users. For example, amapping of item A to item B may be used as a basis to recommend item Bto users who purchase, view, or favorably rate item A. As described inU.S. Pat. No. 7,685,074, referenced above, different datasets ofitem-to-item mappings may be generated based on different types of userbehaviors (purchases, item viewing events, etc.), and these datasets maybe used in various contexts to provide item recommendations to users.For example, the item-to-item mappings may be used to providepersonalized recommendations that are based on a particular user'spurchases, item ratings, and/or other item preference information.

In a typical recommendations scenario, a list of items known to be ofinterest to the user (e.g., items the user has purchased or favorablyrated) are passed to the recommendation service 68, as represented byarrow 70 in FIG. 2. The recommendation service 68 then uses theitem-to-item association mappings 62 to generate a list of recommendeditems to present to the user. As part of this process, therecommendation service 68 may filter out from a preliminaryrecommendations list any items that have already been purchased, rated,or designated as “owned” by the target user.

The recommendation service 68 shown in FIG. 2 may also use other typesof association mappings to generate personalized recommendations. Forexample, the recommendation service 68 may use entity-to-itemassociation mappings, where an “entity” can be, for example, an author,artist or brand. For instance, and entity-to-item mapping of aparticular author to a particular movie may be used as a basis forrecommending the movie to a user who favorably rates, or otherwise showsan interest in, the author. In addition, the recommendation service 68may use entity-to-entity mappings, and/or item-to-entity mappings, togenerate personalized entity recommendations for users. These and othertypes of associations may be mined automatically from the aggregateduser event histories 58, and/or may be created manually by systemadministrators.

As will be recognized, the item rating interface of the presentdisclosure can be used with other types of recommendation andpersonalization services, including recommendation services that do notuse item-to-item association mappings. For example, the item ratinginterface may be used in a system that uses a conventional collaborativerecommendations engine that matches users to similar users as a basisfor selecting items to recommend. Thus, the invention is not limited tosystems that use item-to-item association mappings as a basis forrecommending items.

FIG. 3 illustrates a process that may be implemented by the interactivesystem 50 to generate personalized item rating pages 26 of the typeshown in FIG. 1. This process may be executed whenever a user computingdevice 51 requests an item rating page 26 or requests a refresh of sucha page. In block 80, a set of not-yet-rated items is selected to presentto the user for rating. Any appropriate item selection algorithm may beused for this purpose. For example, the items may be selected from oneor more of the following groups: (1) the most popular items (based onsales, ratings, or some other user activity), (2) personalized itemrecommendations generated for the user by the recommendation service 68or some other recommendation algorithm, and (3) items identified bystaff or by an automated process as being useful for determining uniquecharacteristics of users. Any items that have already been purchased,rated, or tagged as “owned” by the user are filtered out or otherwiseomitted.

In block 82 of FIG. 3, the following steps are performed for eachnot-yet-rated item to generate a corresponding “downstreamrecommendations” list 34. First, a corresponding list of associateditems is looked up from the item-to-item association mappings 62. In oneembodiment, a dataset of purchase-based item-to-item associations isused for this purpose, and the list is a ranked list in which the itemsare ranked in terms of the degree of association with the not-yet-rateditem. Second, this list of associated items is filtered to remove anyitems that are already “known” to the user, such as items that have beenrated, purchased, or tagged as “owned” by the user. (Additionalfiltering may optionally be performed to remove any items that differsignificantly in character from the others.) Third, the filtered list istruncated to the desired length, such as by retaining the top-ranked Nitems (where N is typically in the range of 1 to 10). FIG. 4, which isdiscussed below, describes an alternative method that may be used togenerate the downstream recommendations lists 34. The tasks associatedwith blocks 82 and 84 may be performed by a personalization process 66.

In block 84 of FIG. 3, a server 52 constructs the item rating page 26based on the items identified in blocks 82 and 84. In one embodiment,this involves accessing the catalog service 56 to retrieve the relevantcatalog content (product images, etc.) of the items, and then populatingan item rating page template. Appropriate JavaScript or other executablecode may also be included for updating the page based on mouse-overevents, “Like” events, page refresh events, and other types ofuser-driven events. For example, each popover that is capable of beingdisplayed on the page may be implemented using an HTML Div element, andwith associated JavaScript for hiding and showing this element. In block86, the item rating page is transmitted by a server 52 to the requestinguser computing device 51 for display.

In some embodiments, the item rating page or interface 26 mayalternatively be constructed in whole or in part by a user computingdevice 51 based on information received from the server 52. For example,a mobile shopping app may generate the display page on a smartphone or atablet device.

FIG. 4 illustrates an alternate process that may be used by theinteractive system 50 to generate the downstream recommendations lists34. This alternate process may be used with any recommendation engine orservice that is capable of generating personalized item recommendationsbased in whole or in part on item ratings of users, including but notlimited to recommendation services 52 that use item-to-item mappings 62.In block 90, a recommendation service is invoked to generate a firstranked set of personalized item recommendations (“set A”) based on theuser's existing profile. In block 92, the recommendation service isinvoked to generate a second ranked set of personalized itemrecommendations (“set B”) based on an augmented profile in which theuser is assumed to have favorably rated the not-yet rated item. In block94, these two sets are compared to identify the items (or at least thetop ranked items) that appear in set B but not set A. These are the“downstream recommendation” items 34 that will tend to be recommended tothe user if the user favorably rates the not-yet-rated item in question.

One possible variation to the processes shown in FIGS. 3 and 4 is toanalyze each downstream recommendations list 34 (ideally before thelists are truncated for display) to identify the characterizingattributes of each such list. These characterizing attributes may thenbe communicated to the user, either instead of or in addition to theactual list of items 34. For example, a user may be informed that afavorable rating of a particular item will tend to result inrecommendations of “black dress shoes,” or of “productivity applicationsfor smartphones.”

The various components shown in FIG. 2, and the various processesdescribed above (including those shown in FIGS. 3 and 4) may beimplemented in a computing system via an appropriate combination ofcomputerized machinery (hardware) and executable program code. Forexample, the catalog service 56, association mining service 60,recommendation service 68, and other personalization services 66 mayeach be implemented by one or more physical computing devices (e.g.,servers) programmed with specific executable service code. Each suchcomputing device typically includes one or more processors capable ofexecuting instructions, and a memory capable of storing instructions anddata. The executable code may be stored on any appropriate type or typesof non-transitory computer storage or storage devices, such as magneticdisk drives and solid-state memory arrays. Some of the services andfunctions may alternatively be implemented in application-specificcircuitry (e.g., ASICs or FPGAs). The various databases and datarepositories 54, 58, 62 shown in FIG. 2 may be implemented usingrelational databases, flat file systems, tables, and/or other types ofstorage systems that use non-transitory storage devices (disk drives,solid state memories, etc.) to store data. Each such data repository mayinclude multiple distinct databases. In a typical implementation, therecommendations provided to users, including the downstreamrecommendations 34 presented by the item ratings interface 26, are basedon an automated analysis of many millions of recorded actions of manythousands or millions of users. As explained above, the item ratinginterface 26 may, in some embodiments, be implemented partly or whollyin client-side application code that runs on users' computing devices.

The foregoing embodiments have been presented by way of illustration,and not limitation. Thus, nothing in the foregoing description isintended to imply that any particular feature, characteristic or step isessential to the invention. For example, although portions of thisdisclosure refer to a web site that provides online shoppingfunctionality, the invention is not limited either to web site basedimplementations or to shopping systems.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

What is claimed is:
 1. A computer-implemented method for assisting auser in rating items stored or represented in a computer datarepository, the method comprising: providing a recommendation servicethat generates personalized item recommendations for users based atleast partly on ratings assigned by the users to particular items;selecting an item that has not been rated by the user; generatinginformation regarding one or more additional items that would berecommended to the user by the recommendation service as a result of theuser assigning a particular rating to the selected item; and generating,for the user, a personalized user interface that provides functionalityfor the user to assign at least said particular rating to the selecteditem, and to view, prior to rating the selected item, said informationregarding one or more additional items that would be recommended, thepersonalized user interface thereby enabling the user to assess, priorto rating the selected item, a downstream effect of assigning theparticular rating to the selected item; said method performedprogrammatically by a computing system that comprises one or morecomputing devices.
 2. The method of claim 1, wherein the personalizeduser interface includes an item rating element that can be activated bythe user to assign the particular rating to the selected item, and thepersonalized user interface is responsive to a browsing action in whichthe user selects, but does not activate, the item rating element, bydisplaying the information regarding the one or more additional items.3. The method of claim 2, wherein the browsing action is a mouse-overaction.
 4. The method of claim 2, wherein the personalized userinterface is responsive to the browsing action by generating a popoverthat displays the information regarding the one or more additionalitems.
 5. The method of claim 1, wherein the particular rating is afavorable rating.
 6. The method of claim 1, wherein the particularrating is an unfavorable rating.
 7. The method of claim 1, whereingenerating the information regarding one or more additional itemscomprises selecting the one or more additional items based partly on aprofile of the user.
 8. The method of claim 1, wherein generating theinformation regarding one or more additional items comprises: lookingup, from a pre-generated data repository of item-to-item associationmappings, a set of items that are behaviorally associated with theselected item; and filtering out, from the set of behaviorallyassociated items, one or more items that, based on a profile of theuser, are already known to the user.
 9. The method of claim 1, whereingenerating the information regarding one or more additional itemscomprises: generating, with the recommendation service, a first set ofitem recommendations based on an existing profile of the user;generating, with the recommendation service, a second set of itemrecommendations based on an augmented version of said profile, saidaugmented version reflecting a hypothetical assignment by the user ofthe particular rating to the selected item; and determining, bycomparing the first and second sets of item recommendations, one or moreitems that are included in the second set of item recommendations butnot in the first set of item recommendations.
 10. The method of claim 1,wherein the personalized user interface is a personalized web page. 11.The method of claim 1, wherein the items are catalog items representedin an electronic catalog hosted by the computing system.
 12. Aninteractive system, comprising: a computer system that hosts a browsablerepository of items, said computer system comprising one or morecomputing devices, and being programmed to implement at least: arecommendation service that generates personalized recommendations ofitems for users based at least partly on ratings assigned by the usersto particular items; a personalization process that selects an item thathas not been rated by a user, and generates information regarding one ormore additional items that would be recommended to the user by therecommendation service as a result of the user assigning a particularrating to the selected item; and an item rating interface that providesfunctionality for the user to assign at least the particular rating tothe selected item, and to view, prior to rating the selected item, theinformation regarding one or more additional items that would berecommended, the item rating interface thereby enabling the user toassess, prior to rating the selected item, a downstream effect ofassigning the particular rating to the selected item.
 13. Theinteractive system of claim 12, wherein the item rating interface:includes an item rating element that can be activated by the user toassign the particular rating to the selected item; and is responsive toa browsing action in which the user selects, but does not activate, theitem rating element by displaying the information regarding the one ormore additional items.
 14. The interactive system of claim 12, whereinthe particular rating is a favorable rating.
 15. The interactive systemof claim 12, wherein the particular rating is an unfavorable rating. 16.The interactive system of claim 12, wherein the personalization processis configured to generate the information regarding the one or moreadditional items by at least: looking up, from a pre-generated datarepository of item-to-item mappings, a set of items that are related tothe selected item; and filtering out, from the set of related items, oneor more items that, based on a profile of the user, are already known tothe user.
 17. The interactive system of claim 12, wherein thepersonalization process is configured to generate the informationregarding the one or more additional items by at least: generating, withthe recommendation service, a first set of item recommendations based onan existing profile of the user; generating, with the recommendationservice, a second set of item recommendations based on an augmentedversion of said profile, said augmented version reflecting ahypothetical assignment by the user of the particular rating to theselected item; and determining, by comparing the first and second setsof item recommendations, one or more items that are included in thesecond set of item recommendations but not in the first set of itemrecommendations.
 18. The interactive system of claim 12, wherein theitems are catalog items represented in an electronic catalog.
 19. Anon-transitory computer storage medium that stores executable code thatdirects a user computing device to implement a process that comprises:displaying an item rating interface that includes an item rating elementthat can be activated by a user to assign a particular rating to anitem; detecting a browsing event in which the user selects, but does notactivate, the item rating element; and in response to the browsingevent, displaying information regarding one or more additional itemsthat would be recommended to the user as a result of the user assigningthe particular rating to the item, to thereby enable the user to assess,prior to rating the item, a downstream effect of assigning theparticular rating to the item.
 20. The computer storage medium of claim19, wherein the browsing event is a mouse-over event.
 21. The computerstorage medium of claim 19, wherein the executable code directs thecomputing device to display the information regarding one or moreadditional items in a popover that is displayed in response to thebrowsing event.
 22. The computer storage medium of claim 21, wherein thepopover additionally provides an option for the user to specify that theuser already owns the item.
 23. The computer storage medium of claim 19,in combination with a server system that communicates with the usercomputing device over a network, said server system programmed to useitem association data, in combination with a profile of the user, toidentify the one or more additional items.